Industrial equipment energy-saving method and system based on edge computing

Abstract

The invention discloses an industrial equipment energy-saving method and system based on edge computing. The industrial equipment energy-saving method based on edge computing comprises the following steps: acquiring a historical data set of a historical preset time period of industrial equipment; calculating an energy consumption reference value based on the historical unit gas production energy consumption, the historical fan power and the historical motor input power; dividing the preset time period into a plurality of time windows based on the historical compressed air flow, the historical water inlet and outlet temperature difference, the historical rotating speed and a preset window division standard; based on the historical data set and the energy consumption reference value corresponding to each time window, calculating an optimal path and all other paths except the optimal path in a preset time period through an ant colony algorithm; and on the basis of the preset screening standard, the optimal path and all other paths, the parameter setting value of the current preset time period of the industrial equipment is determined, and the operation efficiency is ensured under the condition of high efficiency and energy conservation.

Description

Technical Field

[0001] This application relates to the technical field of energy saving in industrial equipment, and in particular to an energy-saving method and system for industrial equipment based on edge computing. Background Technology

[0002] In industrial production and the operation of various equipment, air compressors, cooling towers and motors are common high-energy-consuming equipment, and their energy costs account for a large proportion of the total operating costs of enterprises.

[0003] Currently, there is a lack of effective dynamic correlation models to accurately describe the relationship between equipment energy consumption, efficiency, and load, making it difficult to provide optimal control strategies based on actual conditions. Consequently, energy-saving effects and improvements in equipment operating efficiency are limited. Therefore, there is an urgent need for a technical solution that can address these issues and achieve efficient and energy-saving control of equipment such as air compressors, cooling towers, and motors. Summary of the Invention

[0004] This application aims to at least address the technical problems existing in the prior art. To this end, this application proposes an energy-saving method and system for industrial equipment based on edge computing, which can achieve adaptive parameter configuration for different time periods within a preset time cycle, ensuring operational efficiency while achieving high energy efficiency.

[0005] The first aspect of this application provides an energy-saving method for industrial equipment based on edge computing, comprising the following steps:

[0006] Obtain historical datasets for the industrial equipment over a preset time period, wherein the historical datasets include historical unit gas production energy consumption, historical fan power, historical motor input power, historical compressed air flow rate of the air compressor, historical inlet and outlet water temperature difference of the cooling tower, and historical motor speed.

[0007] Energy consumption reference values ​​are calculated based on the historical unit gas production energy consumption, the historical fan power, and the historical motor input power.

[0008] Based on the historical compressed air flow rate, the historical inlet and outlet water temperature difference, the historical rotation speed, and the preset window division criteria, the preset time period is divided into multiple time windows;

[0009] Based on the historical dataset and energy consumption reference value corresponding to each time window, the optimal path and all other paths except the optimal path within the preset time period are calculated using the ant colony algorithm.

[0010] Based on preset screening criteria, the optimal path, and all other paths, the parameter settings for the current preset time period of the industrial equipment are determined, wherein the parameter settings include unit gas production energy consumption, fan power, and motor input power.

[0011] The control method according to the embodiments of this application has at least the following beneficial effects:

[0012] This method acquires historical datasets of industrial equipment over a preset time period and calculates energy consumption reference values ​​based on historical unit gas production energy consumption, historical fan power, and historical motor input power. It divides the preset time period into multiple time windows based on historical compressed air flow rate, historical inlet / outlet water temperature difference, historical rotational speed, and preset window segmentation criteria. By dividing the time period into multiple time windows for parameter configuration, this application achieves adaptive parameter configuration for different time periods. Based on the historical dataset and energy consumption reference values ​​corresponding to each time window, an ant colony algorithm is used to calculate the optimal path and all other paths within the preset time period. Based on preset screening criteria, the optimal path, and all other paths, the parameter settings for the current preset time period of the industrial equipment are determined. These parameter settings include unit gas production energy consumption, fan power, and motor input power. This application selects paths using screening criteria, thereby ensuring operational efficiency while achieving high energy efficiency.

[0013] According to some embodiments of this application, the historical dataset further includes historical load rate, historical cooling amplitude, and historical motor efficiency. The step of determining the parameter settings for the current preset time period of the industrial equipment based on preset screening criteria, the optimal path, and all other paths includes:

[0014] Construct an initial LSTM efficiency prediction model, input the historical dataset into the initial LSTM efficiency prediction model for training, and obtain a trained LSTM efficiency prediction model.

[0015] The parameter settings corresponding to the optimal path and all other paths are input into the trained LSTM efficiency prediction model for prediction, resulting in a set of predicted load rates, a set of predicted cooling amplitudes, and a set of predicted motor efficiencies for each path. The set of predicted load rates for each path includes the predicted load rate for each time window, the set of predicted cooling amplitudes for each path includes the predicted cooling amplitude for each time window, and the set of predicted motor efficiencies includes the predicted motor efficiencies for each time window.

[0016] If the set of predicted loading rates, the set of predicted cooling amplitude, and the set of predicted motor efficiency corresponding to the optimal path meet the preset screening criteria, the parameter setting value corresponding to the optimal path will be used as the parameter setting value for the current preset time period of the industrial equipment.

[0017] According to some embodiments of this application, the edge computing-based energy-saving method for industrial equipment further includes:

[0018] If the predicted load rate set, the predicted cooling amplitude set, and the predicted motor efficiency set corresponding to the optimal path do not meet the preset screening criteria, the path with the best ranking among all other paths and whose corresponding predicted load rate set, predicted cooling amplitude set, and predicted motor efficiency set meet the preset screening criteria shall be selected as the second optimal path.

[0019] The parameter setting value corresponding to the second optimal path is used as the parameter setting value for the current preset time period of the industrial equipment.

[0020] According to some embodiments of this application, the calculation of energy consumption reference values ​​based on the historical unit gas production energy consumption, the historical fan power, and the historical motor input power includes:

[0021] The first energy consumption is obtained by multiplying the average historical unit gas production energy consumption by the first preset weight.

[0022] The second energy consumption is obtained by multiplying the average historical wind turbine power by the second preset weight.

[0023] The third energy consumption is obtained by multiplying the average historical motor input power by the third preset weight.

[0024] The first energy consumption, the second energy consumption, and the third energy consumption are added together to obtain the energy consumption reference value.

[0025] According to some embodiments of this application, the step of dividing the preset time period into multiple time windows based on the historical compressed air flow rate, the historical inlet and outlet water temperature difference, the historical rotational speed, and a preset window division standard includes:

[0026] Based on the historical compressed air flow rate, calculate the average historical compressed air flow rate for each preset time interval; and calculate the difference between the average historical compressed air flow rate for each two adjacent preset time intervals, wherein the preset time interval is less than the preset time period;

[0027] Based on the historical inlet and outlet water temperature difference, calculate the average historical inlet and outlet water temperature difference for each preset time interval; and calculate the difference between the average historical inlet and outlet water temperature differences for each two adjacent preset time intervals.

[0028] Based on the historical rotational speed, calculate the average historical rotational speed for each preset time interval; and calculate the difference between the average historical rotational speeds for each two adjacent preset time intervals.

[0029] Select two adjacent preset time intervals in which the difference between the historical average compressed air flow rate, the difference between the historical average inlet and outlet water temperature, and the difference between the historical average rotational speed do not reach the preset window division criteria, and divide them into a time window;

[0030] Select two adjacent preset time intervals that meet the preset window division criteria, based on the difference between the historical average compressed air flow rate, the difference between the historical average inlet and outlet water temperature difference, or the difference between the historical average rotation speed. Divide these intervals into two corresponding time windows.

[0031] According to some embodiments of this application, the step of calculating the optimal path and all other paths within the preset time period using an ant colony algorithm based on the historical dataset and energy consumption reference value corresponding to each time window includes:

[0032] Based on the historical dataset corresponding to each time window, calculate the average energy consumption value corresponding to each time window;

[0033] Ant colonies are established based on low-power and high-power indicator groups, and the ant colonies are differentiated to obtain differentiated ant colonies. The low-power indicator group is the ant colony whose average energy consumption value corresponding to the time window is less than or equal to the energy consumption reference value, and the high-power indicator group is the ant colony whose average energy consumption value corresponding to the time window is greater than the energy consumption reference value.

[0034] Based on the ant colony algorithm, the pheromone matrix of the differentiated ant colony is calculated;

[0035] Based on the pheromone matrix, path optimization is performed to obtain the optimal path and all other paths within the preset time period.

[0036] A second aspect of this application provides an edge computing-based energy-saving system for industrial equipment, comprising:

[0037] The data acquisition module is used to acquire historical datasets of the industrial equipment over a preset time period. The historical datasets include historical unit gas production energy consumption, historical fan power, historical motor input power, historical compressed air flow rate of the air compressor, historical inlet and outlet water temperature difference of the cooling tower, and historical motor speed.

[0038] The energy consumption reference value calculation module is used to calculate the energy consumption reference value based on the historical unit gas production energy consumption, the historical fan power, and the historical motor input power.

[0039] The time window division module is used to divide the preset time period into multiple time windows based on the historical compressed air flow rate, the historical inlet and outlet water temperature difference, the historical rotational speed and the preset window division standard;

[0040] The path calculation module is used to calculate the optimal path and all other paths except the optimal path within the preset time period based on the historical dataset and energy consumption reference value corresponding to each time window, using the ant colony algorithm.

[0041] The parameter value determination module is used to determine the parameter setting values ​​of the industrial equipment for the current preset time period based on the preset screening criteria, the optimal path, and all other paths. The parameter setting values ​​include unit gas production energy consumption, fan power, and motor input power.

[0042] This system acquires historical datasets of industrial equipment over a preset time period and calculates energy consumption reference values ​​based on historical unit gas production energy consumption, historical fan power, and historical motor input power. It divides the preset time period into multiple time windows based on historical compressed air flow rate, historical inlet / outlet water temperature difference, historical rotational speed, and preset window segmentation criteria. This application achieves adaptive parameter configuration for different time periods by dividing the system into multiple time windows. Based on the historical dataset and energy consumption reference values ​​corresponding to each time window, it calculates the optimal path and all other paths within the preset time period using an ant colony algorithm. Based on preset screening criteria, the optimal path, and all other paths, it determines the parameter settings for the current preset time period of the industrial equipment. These parameter settings include unit gas production energy consumption, fan power, and motor input power. This application selects paths using screening criteria, thereby ensuring operational efficiency while maintaining high energy efficiency.

[0043] A third aspect of this application provides an edge computing-based energy-saving electronic device for industrial equipment, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which are executed by the at least one control processor to enable the at least one control processor to perform the aforementioned edge computing-based energy-saving method for industrial equipment.

[0044] A fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the aforementioned edge computing-based energy-saving method for industrial equipment.

[0045] It should be noted that the beneficial effects of the second to fourth aspects of this application with respect to the prior art are the same as the beneficial effects of the aforementioned edge computing-based industrial equipment energy-saving system with respect to the prior art, and will not be described in detail here.

[0046] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0047] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0048] Figure 1 This is a flowchart of an embodiment of an energy-saving method for industrial equipment based on edge computing according to this application;

[0049] Figure 2 This is a schematic diagram of an embodiment of an energy-saving system for industrial equipment based on edge computing provided in this application;

[0050] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device provided in this application.

[0051] Explanation of reference numerals in the attached figures: data acquisition module 1100, energy consumption reference value calculation module 1200, time window division module 1300, path calculation module 1400, parameter value determination module 1500, processor 301, memory 302, communication interface 303, bus 310. Detailed Implementation

[0052] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0053] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0054] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0055] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0056] In industrial production and the operation of various equipment, air compressors, cooling towers and motors are common high-energy-consuming equipment, and their energy costs account for a large proportion of the total operating costs of enterprises.

[0057] Currently, there is a lack of effective dynamic correlation models to accurately describe the relationship between equipment energy consumption, efficiency, and load, making it difficult to provide optimal control strategies based on actual conditions. Consequently, energy-saving effects and improvements in equipment operating efficiency are limited. Therefore, there is an urgent need for a technical solution that can address these issues and achieve efficient and energy-saving control of equipment such as air compressors, cooling towers, and motors.

[0058] Please see Figure 1 This is a flowchart illustrating an energy-saving method for industrial equipment based on edge computing, provided in an embodiment of this application. The method is applied to electronic devices, such as servers. Figure 1 As shown, this edge computing-based energy-saving method for industrial equipment includes:

[0059] Step S101: Obtain historical datasets of industrial equipment for a preset time period. The historical datasets include historical unit gas production energy consumption, historical fan power, historical motor input power, historical compressed air flow rate of air compressor, historical inlet and outlet water temperature difference of cooling tower, and historical motor speed.

[0060] Step S102: Calculate the energy consumption reference value based on historical unit gas production energy consumption, historical fan power, and historical motor input power;

[0061] Step S103: Based on historical compressed air flow rate, historical inlet and outlet water temperature difference, historical rotation speed and preset window division criteria, the preset time period is divided into multiple time windows;

[0062] Step S104: Based on the historical dataset and energy consumption reference value corresponding to each time window, calculate the optimal path and all other paths except the optimal path within the preset time period using the ant colony algorithm.

[0063] Step S105: Based on the preset screening criteria, the optimal path, and all other paths, determine the parameter settings for the current preset time period of the industrial equipment. The parameter settings include unit gas production energy consumption, fan power, and motor input power.

[0064] The aforementioned acquisition of historical datasets for industrial equipment over a preset time period can be achieved by receiving historical energy-saving data collected by sensors deployed on air compressors, cooling towers, and motors via edge nodes, and preprocessing the historical energy-saving data to obtain historical datasets for industrial equipment over a preset time period.

[0065] The parameter settings for determining the current preset time period of industrial equipment based on preset screening criteria, the optimal path, and all other paths can be as follows:

[0066] Construct an initial LSTM efficiency prediction model, input the historical dataset into the initial LSTM efficiency prediction model for training, and obtain a trained LSTM efficiency prediction model.

[0067] The parameter settings corresponding to the optimal path and all other paths are input into the trained LSTM efficiency prediction model for prediction, resulting in a set of predicted loading rates, a set of predicted cooling amplitudes, and a set of predicted motor efficiencies for each path. The set of predicted loading rates for each path includes the predicted loading rate for each time window, the set of predicted cooling amplitudes for each path includes the predicted cooling amplitude for each time window, and the set of predicted motor efficiencies includes the predicted motor efficiencies for each time window.

[0068] If the set of predicted load rates, the set of predicted cooling amplitude, and the set of predicted motor efficiency corresponding to the optimal path meet the preset screening criteria, the parameter setting value corresponding to the optimal path will be used as the parameter setting value for the current preset time period of the industrial equipment.

[0069] The current preset time period can be any one of a day, a month, or a year.

[0070] The above describes the construction of an initial LSTM efficiency prediction model. Historical datasets are then input into this initial LSTM efficiency prediction model for training, resulting in a trained LSTM efficiency prediction model. This process includes:

[0071] The historical dataset is input into the initial LSTM efficiency prediction model for training until the training times reach the preset number, resulting in a trained LSTM efficiency prediction model. The historical dataset also includes historical loading rate, historical cooling amplitude, and historical motor efficiency. The LSTM efficiency prediction model is used to predict the loading rate, predicted cooling amplitude, and predicted motor efficiency for each time window.

[0072] This method acquires historical datasets of industrial equipment over a preset time period and calculates energy consumption reference values ​​based on historical unit gas production energy consumption, historical fan power, and historical motor input power. It divides the preset time period into multiple time windows based on historical compressed air flow rate, historical inlet / outlet water temperature difference, historical rotational speed, and preset window segmentation criteria. By dividing the time period into multiple time windows for parameter configuration, this application achieves adaptive parameter configuration for different time periods. Based on the historical dataset and energy consumption reference values ​​corresponding to each time window, an ant colony algorithm is used to calculate the optimal path and all other paths within the preset time period. Based on preset screening criteria, the optimal path, and all other paths, the parameter settings for the current preset time period of the industrial equipment are determined. These parameter settings include unit gas production energy consumption, fan power, and motor input power. This application selects paths using screening criteria, thereby ensuring operational efficiency while achieving high energy efficiency.

[0073] In some embodiments, the edge computing-based energy-saving method for industrial equipment further includes:

[0074] If the set of predicted load rate, the set of predicted cooling amplitude, and the set of predicted motor efficiency corresponding to the optimal path do not meet the preset screening criteria, the path with the best ranking among all other paths and whose corresponding set of predicted load rate, the set of predicted cooling amplitude, and the set of predicted motor efficiency meet the preset screening criteria shall be selected as the second optimal path.

[0075] The parameter settings corresponding to the second optimal path are used as the parameter settings for the current preset time period of the industrial equipment.

[0076] This application achieves high efficiency while ensuring operational efficiency by combining historical load rate, historical cooling amplitude, and historical motor efficiency to select the path.

[0077] In some embodiments, an energy consumption reference value is calculated based on historical unit gas production energy consumption, historical fan power, and historical motor input power, including:

[0078] The first energy consumption is obtained by multiplying the historical average unit gas production energy consumption by the first preset weight.

[0079] The second energy consumption is obtained by multiplying the average historical wind turbine power by the second preset weight;

[0080] The third energy consumption is obtained by multiplying the average historical motor input power by the third preset weight.

[0081] Add the first energy consumption, the second energy consumption, and the third energy consumption together to obtain the energy consumption reference value.

[0082] The first preset weight, the second preset weight, and the third preset weight mentioned above are three pre-set arbitrary constants that sum to one.

[0083] This application improves the accuracy of energy consumption reference values ​​by assigning different weight values ​​to different energy consumption levels and calculating energy consumption reference values.

[0084] In some embodiments, based on historical compressed air flow rate, historical inlet and outlet water temperature difference, historical rotational speed, and a preset window division standard, the preset time period is divided into multiple time windows, including:

[0085] Based on historical compressed air flow rate, calculate the average historical compressed air flow rate for each preset time interval; and calculate the difference between the average historical compressed air flow rate for each two adjacent preset time intervals, where the preset time interval is less than the preset time period.

[0086] Based on the historical inlet and outlet water temperature difference, calculate the average historical inlet and outlet water temperature difference for each preset time interval; and calculate the difference between the average historical inlet and outlet water temperature differences for each two adjacent preset time intervals.

[0087] Based on historical rotational speeds, calculate the average historical rotational speed for each preset time interval; and calculate the difference between the average historical rotational speeds for each two adjacent preset time intervals.

[0088] Select two adjacent time intervals where the difference between the historical average compressed air flow rate, the historical average inlet and outlet water temperature difference, or the historical average speed does not meet the preset window division criteria, and divide them into one time window.

[0089] Select two adjacent preset time intervals that meet the preset window division criteria based on the difference between the historical average compressed air flow rate, the difference between the historical average inlet and outlet water temperature, or the difference between the historical average rotation speed. Divide these intervals into two corresponding time windows.

[0090] This application divides the window by using historical compressed air flow rate, historical inlet and outlet water temperature difference, and historical rotational speed, thereby enabling adaptive parameter configuration under different load conditions.

[0091] In some embodiments, based on the historical dataset and energy consumption reference values ​​corresponding to each time window, the optimal path and all other paths within a preset time period are calculated using the ant colony algorithm, including:

[0092] Based on the historical dataset corresponding to each time window, calculate the average energy consumption value for each time window.

[0093] Ant colonies are established based on low-power and high-power indicator groups, and the ant colonies are differentiated to obtain differentiated ant colonies. The low-power indicator group is the ant colony whose average energy consumption value corresponding to the time window is less than or equal to the energy consumption reference value, and the high-power indicator group is the ant colony whose average energy consumption value corresponding to the time window is greater than the energy consumption reference value.

[0094] Based on the ant colony algorithm, the pheromone matrix of the differentiated ant colony is calculated.

[0095] Path optimization is performed based on the pheromone matrix to obtain the optimal path and all other paths within a preset time period.

[0096] Specifically, the formula for calculating the pheromone matrix of the differentiated ant colony based on the ant colony algorithm can be as follows:

[0097]

[0098] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij

[0099]

[0100] in, Let ω be the probability that the k-th ant in the t-th generation travels from city i to city j, ω be the importance of the pheromone, θ be the relative importance of the heuristic factor, and τ be the probability of the ant traveling from city i to city j. ij (t) represents the pheromones from city i to city j in the t-th generation of ants, n ij (t) represents the heuristic factor from city i to city j in the t-th generation of ants, J k (i) represents the cities that ant k can currently choose from, and d ij (t) represents the distance from city i to city j, Δτ ij Let m be the total pheromone left by m ants along the path from city i to city j. Let L be the pheromone left by the k-th ant on the path from city i to city j, Q be the total pheromone possessed by the k-th ant, ρ be the pheromone dilution factor ranging from 0 to 1, and L be the pheromone level. k Let be the total distance of the path traveled by the k-th ant.

[0101] Additionally, refer to Figure 2 One embodiment of this application provides an energy-saving system for industrial equipment based on edge computing, including a data acquisition module 1100, an energy consumption reference value calculation module 1200, a time window division module 1300, a path calculation module 1400, and a parameter value determination module 1500, wherein:

[0102] The data acquisition module 1100 is used to acquire historical datasets of industrial equipment over a preset time period. The historical datasets include historical unit gas production energy consumption, historical fan power, historical motor input power, historical compressed air flow of air compressor, historical inlet and outlet water temperature difference of cooling tower, and historical motor speed.

[0103] The energy consumption reference value calculation module 1200 is used to calculate the energy consumption reference value based on historical unit gas production energy consumption, historical fan power and historical motor input power;

[0104] The time window division module 1300 is used to divide a preset time period into multiple time windows based on historical compressed air flow, historical inlet and outlet water temperature difference, historical speed and preset window division criteria.

[0105] The path calculation module 1400 is used to calculate the optimal path and all other paths except the optimal path within a preset time period based on the historical dataset and energy consumption reference value corresponding to each time window, using the ant colony algorithm.

[0106] The parameter value determination module 1500 is used to determine the parameter setting values ​​of the industrial equipment for the current preset time period based on preset screening criteria, the optimal path and all other paths. The parameter setting values ​​include unit gas production energy consumption, fan power and motor input power.

[0107] This system acquires historical datasets of industrial equipment over a preset time period and calculates energy consumption reference values ​​based on historical unit gas production energy consumption, historical fan power, and historical motor input power. It divides the preset time period into multiple time windows based on historical compressed air flow rate, historical inlet / outlet water temperature difference, historical rotational speed, and preset window segmentation criteria. This application achieves adaptive parameter configuration for different time periods by dividing the system into multiple time windows. Based on the historical dataset and energy consumption reference values ​​corresponding to each time window, it calculates the optimal path and all other paths within the preset time period using an ant colony algorithm. Based on preset screening criteria, the optimal path, and all other paths, it determines the parameter settings for the current preset time period of the industrial equipment. These parameter settings include unit gas production energy consumption, fan power, and motor input power. This application selects paths using screening criteria, thereby ensuring operational efficiency while maintaining high energy efficiency.

[0108] It should be noted that the system embodiments described above are based on the same inventive concept as the method embodiments described above. Therefore, the relevant content of the method embodiments described above is also applicable to the system embodiments described above, and will not be repeated here.

[0109] Figure 3 A schematic diagram of the hardware structure for energy saving of industrial equipment based on edge computing provided in an embodiment of this application is shown.

[0110] Energy-saving devices for industrial equipment based on edge computing may include a processor 301 and a memory 302 storing computer program instructions.

[0111] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0112] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory.

[0113] In some embodiments, memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0114] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any of the edge computing-based energy-saving methods for industrial equipment in the above embodiments.

[0115] In one example, the energy-saving device for industrial equipment based on edge computing may further include a communication interface 303 and a bus 310. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0116] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0117] Bus 310 includes hardware, software, or both, that couples components of edge computing-based energy-saving industrial equipment together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0118] This edge computing-based energy-saving industrial equipment can execute the edge computing-based energy-saving method for industrial equipment in this application embodiment based on a three-dimensional design model, thereby achieving a combination of... Figure 1 and Figure 2 The paper describes an energy-saving method and system for industrial equipment based on edge computing.

[0119] Furthermore, in conjunction with the edge computing-based energy-saving methods for industrial equipment described in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the edge computing-based energy-saving methods for industrial equipment described in the above embodiments.

[0120] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0121] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0122] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0123] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0124] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. An energy-saving method for industrial equipment based on edge computing, characterized in that, The energy-saving method for industrial equipment based on edge computing includes: Obtain historical datasets for the industrial equipment over a preset time period, wherein the historical datasets include historical unit gas production energy consumption, historical fan power, historical motor input power, historical compressed air flow rate of the air compressor, historical inlet and outlet water temperature difference of the cooling tower, and historical motor speed. Energy consumption reference values ​​are calculated based on the historical unit gas production energy consumption, the historical fan power, and the historical motor input power. Based on the historical compressed air flow rate, the historical inlet and outlet water temperature difference, the historical rotation speed, and the preset window division criteria, the preset time period is divided into multiple time windows; Based on the historical dataset and energy consumption reference value corresponding to each time window, the optimal path and all other paths except the optimal path within the preset time period are calculated using the ant colony algorithm. Based on preset screening criteria, the optimal path, and all other paths, the parameter settings for the current preset time period of the industrial equipment are determined, wherein the parameter settings include unit gas production energy consumption, fan power, and motor input power.

2. The energy-saving method for industrial equipment based on edge computing according to claim 1, characterized in that, The historical dataset also includes historical load rate, historical cooling amplitude, and historical motor efficiency. The parameter settings for determining the current preset time period of the industrial equipment based on preset screening criteria, the optimal path, and all other paths include: Construct an initial LSTM efficiency prediction model, input the historical dataset into the initial LSTM efficiency prediction model for training, and obtain a trained LSTM efficiency prediction model. The parameter settings corresponding to the optimal path and all other paths are input into the trained LSTM efficiency prediction model for prediction, resulting in a set of predicted load rates, a set of predicted cooling amplitudes, and a set of predicted motor efficiencies for each path. The set of predicted load rates for each path includes the predicted load rate for each time window, the set of predicted cooling amplitudes for each path includes the predicted cooling amplitude for each time window, and the set of predicted motor efficiencies includes the predicted motor efficiencies for each time window. If the set of predicted loading rates, the set of predicted cooling amplitude, and the set of predicted motor efficiency corresponding to the optimal path meet the preset screening criteria, the parameter setting value corresponding to the optimal path will be used as the parameter setting value for the current preset time period of the industrial equipment.

3. The energy-saving method for industrial equipment based on edge computing according to claim 2, characterized in that, The edge computing-based energy-saving method for industrial equipment also includes: If the predicted load rate set, the predicted cooling amplitude set, and the predicted motor efficiency set corresponding to the optimal path do not meet the preset screening criteria, the path with the best ranking among all other paths and whose corresponding predicted load rate set, predicted cooling amplitude set, and predicted motor efficiency set meet the preset screening criteria shall be selected as the second optimal path. The parameter setting value corresponding to the second optimal path is used as the parameter setting value for the current preset time period of the industrial equipment.

4. The energy-saving method for industrial equipment based on edge computing according to claim 1, characterized in that, The calculation of energy consumption reference values ​​based on the historical unit gas production energy consumption, the historical fan power, and the historical motor input power includes: The first energy consumption is obtained by multiplying the average historical unit gas production energy consumption by the first preset weight. The second energy consumption is obtained by multiplying the average historical wind turbine power by the second preset weight. The third energy consumption is obtained by multiplying the average historical motor input power by the third preset weight. The first energy consumption, the second energy consumption, and the third energy consumption are added together to obtain the energy consumption reference value.

5. The energy-saving method for industrial equipment based on edge computing according to claim 1, characterized in that, The preset time period is divided into multiple time windows based on the historical compressed air flow rate, the historical inlet and outlet water temperature difference, the historical rotational speed, and a preset window division standard, including: Based on the historical compressed air flow rate, calculate the average historical compressed air flow rate for each preset time interval; and calculate the difference between the average historical compressed air flow rate for each two adjacent preset time intervals, wherein the preset time interval is less than the preset time period; Based on the historical inlet and outlet water temperature difference, calculate the average historical inlet and outlet water temperature difference for each preset time interval; and calculate the difference between the average historical inlet and outlet water temperature differences for each two adjacent preset time intervals. Based on the historical rotational speed, calculate the average historical rotational speed for each preset time interval; and calculate the difference between the average historical rotational speeds for each two adjacent preset time intervals. Select two adjacent preset time intervals in which the difference between the historical average compressed air flow rate, the difference between the historical average inlet and outlet water temperature, and the difference between the historical average rotational speed do not reach the preset window division criteria, and divide them into a time window; Select two adjacent preset time intervals that meet the preset window division criteria, based on the difference between the historical average compressed air flow rate, the difference between the historical average inlet and outlet water temperature difference, or the difference between the historical average rotation speed. Divide these intervals into two corresponding time windows.

6. The energy-saving method for industrial equipment based on edge computing according to claim 4, characterized in that, The step of calculating the optimal path and all other paths within the preset time period using the ant colony algorithm, based on the historical dataset and energy consumption reference value corresponding to each time window, includes: Based on the historical dataset corresponding to each time window, calculate the average energy consumption value corresponding to each time window; Ant colonies are established based on low-power and high-power indicator groups, and the ant colonies are differentiated to obtain differentiated ant colonies. The low-power indicator group is the ant colony whose average energy consumption value corresponding to the time window is less than or equal to the energy consumption reference value, and the high-power indicator group is the ant colony whose average energy consumption value corresponding to the time window is greater than the energy consumption reference value. Based on the ant colony algorithm, the pheromone matrix of the differentiated ant colony is calculated; Based on the pheromone matrix, path optimization is performed to obtain the optimal path and all other paths within the preset time period.

7. An energy-saving system for industrial equipment based on edge computing, characterized in that, The edge computing-based energy-saving system for industrial equipment includes: The data acquisition module is used to acquire historical datasets of the industrial equipment over a preset time period. The historical datasets include historical unit gas production energy consumption, historical fan power, historical motor input power, historical compressed air flow rate of the air compressor, historical inlet and outlet water temperature difference of the cooling tower, and historical motor speed. The energy consumption reference value calculation module is used to calculate the energy consumption reference value based on the historical unit gas production energy consumption, the historical fan power, and the historical motor input power. The time window division module is used to divide the preset time period into multiple time windows based on the historical compressed air flow rate, the historical inlet and outlet water temperature difference, the historical rotation speed and the preset window division standard; The path calculation module is used to calculate the optimal path and all other paths except the optimal path within the preset time period based on the historical dataset and energy consumption reference value corresponding to each time window, using the ant colony algorithm. The parameter value determination module is used to determine the parameter setting values ​​of the industrial equipment for the current preset time period based on the preset screening criteria, the optimal path, and all other paths. The parameter setting values ​​include unit gas production energy consumption, fan power, and motor input power.

8. An energy-saving device for industrial equipment based on edge computing, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform an edge computing-based energy-saving method for industrial equipment as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for causing a computer to perform an energy-saving method for industrial equipment based on edge computing as described in any one of claims 1 to 6.

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